{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Содержание<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Подготовка-данных\" data-toc-modified-id=\"Подготовка-данных-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Подготовка данных</a></span></li><li><span><a href=\"#Исследование-задачи\" data-toc-modified-id=\"Исследование-задачи-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Исследование задачи</a></span></li><li><span><a href=\"#Борьба-с-дисбалансом\" data-toc-modified-id=\"Борьба-с-дисбалансом-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Борьба с дисбалансом</a></span><ul class=\"toc-item\"><li><span><a href=\"#Изучение-моделей-логистической-регрессии-при-учёте-дисбаланса\" data-toc-modified-id=\"Изучение-моделей-логистической-регрессии-при-учёте-дисбаланса-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Изучение моделей логистической регрессии при учёте дисбаланса</a></span></li><li><span><a href=\"#Изучение-моделей-случайного-леса-при-учёте-дисбаланса\" data-toc-modified-id=\"Изучение-моделей-случайного-леса-при-учёте-дисбаланса-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Изучение моделей случайного леса при учёте дисбаланса</a></span></li></ul></li><li><span><a href=\"#Тестирование-модели\" data-toc-modified-id=\"Тестирование-модели-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Тестирование модели</a></span></li><li><span><a href=\"#Чек-лист-готовности-проекта\" data-toc-modified-id=\"Чек-лист-готовности-проекта-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Чек-лист готовности проекта</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Отток клиентов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Из «Бета-Банка» стали уходить клиенты. Каждый месяц. Немного, но заметно. Банковские маркетологи посчитали: сохранять текущих клиентов дешевле, чем привлекать новых.\n",
    "\n",
    "Нужно спрогнозировать, уйдёт клиент из банка в ближайшее время или нет. Вам предоставлены исторические данные о поведении клиентов и расторжении договоров с банком. \n",
    "\n",
    "Постройте модель с предельно большим значением *F1*-меры. Чтобы сдать проект успешно, нужно довести метрику до 0.59. Проверьте *F1*-меру на тестовой выборке самостоятельно.\n",
    "\n",
    "Дополнительно измеряйте *AUC-ROC*, сравнивайте её значение с *F1*-мерой.\n",
    "\n",
    "Источник данных: [https://www.kaggle.com/barelydedicated/bank-customer-churn-modeling](https://www.kaggle.com/barelydedicated/bank-customer-churn-modeling)\n",
    "\n",
    "Ссылка на репозиторий GitHub: https://github.com/samohod4ik/beta_bank_project\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для решения задачи необходимо построить модель с максимально большим значением *F1*-меры. Заказчик требует довести метрику до 60%. Первым шагом будет получение и изучение данных для обучения моделей. Для обучения модели необходимо кодировать категориальные значения, а также исключить пропуски в данных. Далее разделим кодированный датафрейм на обучающую, валидационную и тестовые выборки. Проверим данные на дисбаланс, исследуем его влияние на поведение модели, уменьшим влияние данного дисбаланса. Так как перед нами стоит задача классификации, то обучим несколько моделей (Логистическая регрессия, Дерево решений и Случайный лес) и выделим лучшую с помощью метрики *F1*. Проверим *F1*-меру на тестовой выборке самостоятельно (предварительно дообучив модель на валидационной выборке, т.к. чем больше данных - тем лучше), затем измерим *AUC-ROC*, сравним её значение с *F1*-мерой."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "from pandas.api.types import is_string_dtype\n",
    "\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.exceptions import DataConversionWarning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, roc_curve, roc_auc_score, auc\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, KFold, GridSearchCV\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.pipeline import Pipeline, make_pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для начала отключим предупреждения от sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(action='ignore', category=DataConversionWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    df = pd.read_csv('/datasets/Churn.csv')\n",
    "except Exception:\n",
    "    df = pd.read_csv('https://code.s3.yandex.net/datasets/Churn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def describe_dataframe(dataframe):\n",
    "    display(dataframe.head(10))\n",
    "    display(dataframe.info())\n",
    "    display(dataframe.describe(percentiles=[.5]).T)\n",
    "    print(f\"Количество дублированных строк: {dataframe.duplicated().sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n30          31    15589475    Azikiwe          591     Spain  Female   39   \n48          49    15766205        Yin          550   Germany    Male   38   \n51          52    15768193  Trevisani          585   Germany    Male   36   \n53          54    15702298   Parkhill          655   Germany    Male   41   \n60          61    15651280     Hunter          742   Germany    Male   35   \n82          83    15641732      Mills          543    France  Female   36   \n85          86    15805254    Ndukaku          652     Spain  Female   75   \n94          95    15676966      Capon          730     Spain    Male   42   \n99         100    15633059    Fanucci          413    France    Male   34   \n111        112    15665790   Rowntree          538   Germany    Male   39   \n\n     Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n30      NaN       0.00              3          1               0   \n48      NaN  103391.38              1          0               1   \n51      NaN  146050.97              2          0               0   \n53      NaN  125561.97              1          0               0   \n60      NaN  136857.00              1          0               0   \n82      NaN       0.00              2          0               0   \n85      NaN       0.00              2          1               1   \n94      NaN       0.00              2          0               1   \n99      NaN       0.00              2          0               0   \n111     NaN  108055.10              2          1               0   \n\n     EstimatedSalary  Exited  \n30         140469.38       1  \n48          90878.13       0  \n51          86424.57       0  \n53         164040.94       1  \n60          84509.57       0  \n82          26019.59       0  \n85         114675.75       0  \n94          85982.47       0  \n99           6534.18       0  \n111         27231.26       0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>RowNumber</th>\n      <th>CustomerId</th>\n      <th>Surname</th>\n      <th>CreditScore</th>\n      <th>Geography</th>\n      <th>Gender</th>\n      <th>Age</th>\n      <th>Tenure</th>\n      <th>Balance</th>\n      <th>NumOfProducts</th>\n      <th>HasCrCard</th>\n      <th>IsActiveMember</th>\n      <th>EstimatedSalary</th>\n      <th>Exited</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>30</th>\n      <td>31</td>\n      <td>15589475</td>\n      <td>Azikiwe</td>\n      <td>591</td>\n      <td>Spain</td>\n      <td>Female</td>\n      <td>39</td>\n      <td>NaN</td>\n      <td>0.00</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>140469.38</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>49</td>\n      <td>15766205</td>\n      <td>Yin</td>\n      <td>550</td>\n      <td>Germany</td>\n      <td>Male</td>\n      <td>38</td>\n      <td>NaN</td>\n      <td>103391.38</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>90878.13</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>52</td>\n      <td>15768193</td>\n      <td>Trevisani</td>\n      <td>585</td>\n      <td>Germany</td>\n      <td>Male</td>\n      <td>36</td>\n      <td>NaN</td>\n      <td>146050.97</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>86424.57</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>54</td>\n      <td>15702298</td>\n      <td>Parkhill</td>\n      <td>655</td>\n      <td>Germany</td>\n      <td>Male</td>\n      <td>41</td>\n      <td>NaN</td>\n      <td>125561.97</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>164040.94</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>61</td>\n      <td>15651280</td>\n      <td>Hunter</td>\n      <td>742</td>\n      <td>Germany</td>\n      <td>Male</td>\n      <td>35</td>\n      <td>NaN</td>\n      <td>136857.00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>84509.57</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>83</td>\n      <td>15641732</td>\n      <td>Mills</td>\n      <td>543</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>36</td>\n      <td>NaN</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>26019.59</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>86</td>\n      <td>15805254</td>\n      <td>Ndukaku</td>\n      <td>652</td>\n      <td>Spain</td>\n      <td>Female</td>\n      <td>75</td>\n      <td>NaN</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>114675.75</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>95</td>\n      <td>15676966</td>\n      <td>Capon</td>\n      <td>730</td>\n      <td>Spain</td>\n      <td>Male</td>\n      <td>42</td>\n      <td>NaN</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>85982.47</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>100</td>\n      <td>15633059</td>\n      <td>Fanucci</td>\n      <td>413</td>\n      <td>France</td>\n      <td>Male</td>\n      <td>34</td>\n      <td>NaN</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6534.18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>112</td>\n      <td>15665790</td>\n      <td>Rowntree</td>\n      <td>538</td>\n      <td>Germany</td>\n      <td>Male</td>\n      <td>39</td>\n      <td>NaN</td>\n      <td>108055.10</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>27231.26</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"Tenure.isna()\").head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n0          1    15634602  Hargrave          619    France  Female   42   \n1          2    15647311      Hill          608     Spain  Female   41   \n2          3    15619304      Onio          502    France  Female   42   \n3          4    15701354      Boni          699    France  Female   39   \n4          5    15737888  Mitchell          850     Spain  Female   43   \n5          6    15574012       Chu          645     Spain    Male   44   \n6          7    15592531  Bartlett          822    France    Male   50   \n7          8    15656148    Obinna          376   Germany  Female   29   \n8          9    15792365        He          501    France    Male   44   \n9         10    15592389        H?          684    France    Male   27   \n\n   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n0     2.0       0.00              1          1               1   \n1     1.0   83807.86              1          0               1   \n2     8.0  159660.80              3          1               0   \n3     1.0       0.00              2          0               0   \n4     2.0  125510.82              1          1               1   \n5     8.0  113755.78              2          1               0   \n6     7.0       0.00              2          1               1   \n7     4.0  115046.74              4          1               0   \n8     4.0  142051.07              2          0               1   \n9     2.0  134603.88              1          1               1   \n\n   EstimatedSalary  Exited  \n0        101348.88       1  \n1        112542.58       0  \n2        113931.57       1  \n3         93826.63       0  \n4         79084.10       0  \n5        149756.71       1  \n6         10062.80       0  \n7        119346.88       1  \n8         74940.50       0  \n9         71725.73       0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>RowNumber</th>\n      <th>CustomerId</th>\n      <th>Surname</th>\n      <th>CreditScore</th>\n      <th>Geography</th>\n      <th>Gender</th>\n      <th>Age</th>\n      <th>Tenure</th>\n      <th>Balance</th>\n      <th>NumOfProducts</th>\n      <th>HasCrCard</th>\n      <th>IsActiveMember</th>\n      <th>EstimatedSalary</th>\n      <th>Exited</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>15634602</td>\n      <td>Hargrave</td>\n      <td>619</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>42</td>\n      <td>2.0</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>101348.88</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>15647311</td>\n      <td>Hill</td>\n      <td>608</td>\n      <td>Spain</td>\n      <td>Female</td>\n      <td>41</td>\n      <td>1.0</td>\n      <td>83807.86</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>112542.58</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>15619304</td>\n      <td>Onio</td>\n      <td>502</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>42</td>\n      <td>8.0</td>\n      <td>159660.80</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113931.57</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>15701354</td>\n      <td>Boni</td>\n      <td>699</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>39</td>\n      <td>1.0</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>93826.63</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>15737888</td>\n      <td>Mitchell</td>\n      <td>850</td>\n      <td>Spain</td>\n      <td>Female</td>\n      <td>43</td>\n      <td>2.0</td>\n      <td>125510.82</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>79084.10</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>15574012</td>\n      <td>Chu</td>\n      <td>645</td>\n      <td>Spain</td>\n      <td>Male</td>\n      <td>44</td>\n      <td>8.0</td>\n      <td>113755.78</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>149756.71</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>15592531</td>\n      <td>Bartlett</td>\n      <td>822</td>\n      <td>France</td>\n      <td>Male</td>\n      <td>50</td>\n      <td>7.0</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>10062.80</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>15656148</td>\n      <td>Obinna</td>\n      <td>376</td>\n      <td>Germany</td>\n      <td>Female</td>\n      <td>29</td>\n      <td>4.0</td>\n      <td>115046.74</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>119346.88</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>15792365</td>\n      <td>He</td>\n      <td>501</td>\n      <td>France</td>\n      <td>Male</td>\n      <td>44</td>\n      <td>4.0</td>\n      <td>142051.07</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>74940.50</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>15592389</td>\n      <td>H?</td>\n      <td>684</td>\n      <td>France</td>\n      <td>Male</td>\n      <td>27</td>\n      <td>2.0</td>\n      <td>134603.88</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>71725.73</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        10000 non-null  int64  \n",
      " 1   CustomerId       10000 non-null  int64  \n",
      " 2   Surname          10000 non-null  object \n",
      " 3   CreditScore      10000 non-null  int64  \n",
      " 4   Geography        10000 non-null  object \n",
      " 5   Gender           10000 non-null  object \n",
      " 6   Age              10000 non-null  int64  \n",
      " 7   Tenure           9091 non-null   float64\n",
      " 8   Balance          10000 non-null  float64\n",
      " 9   NumOfProducts    10000 non-null  int64  \n",
      " 10  HasCrCard        10000 non-null  int64  \n",
      " 11  IsActiveMember   10000 non-null  int64  \n",
      " 12  EstimatedSalary  10000 non-null  float64\n",
      " 13  Exited           10000 non-null  int64  \n",
      "dtypes: float64(3), int64(8), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": "None"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "                   count          mean           std          min  \\\nRowNumber        10000.0  5.000500e+03   2886.895680         1.00   \nCustomerId       10000.0  1.569094e+07  71936.186123  15565701.00   \nCreditScore      10000.0  6.505288e+02     96.653299       350.00   \nAge              10000.0  3.892180e+01     10.487806        18.00   \nTenure            9091.0  4.997690e+00      2.894723         0.00   \nBalance          10000.0  7.648589e+04  62397.405202         0.00   \nNumOfProducts    10000.0  1.530200e+00      0.581654         1.00   \nHasCrCard        10000.0  7.055000e-01      0.455840         0.00   \nIsActiveMember   10000.0  5.151000e-01      0.499797         0.00   \nEstimatedSalary  10000.0  1.000902e+05  57510.492818        11.58   \nExited           10000.0  2.037000e-01      0.402769         0.00   \n\n                          50%          max  \nRowNumber        5.000500e+03     10000.00  \nCustomerId       1.569074e+07  15815690.00  \nCreditScore      6.520000e+02       850.00  \nAge              3.700000e+01        92.00  \nTenure           5.000000e+00        10.00  \nBalance          9.719854e+04    250898.09  \nNumOfProducts    1.000000e+00         4.00  \nHasCrCard        1.000000e+00         1.00  \nIsActiveMember   1.000000e+00         1.00  \nEstimatedSalary  1.001939e+05    199992.48  \nExited           0.000000e+00         1.00  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>50%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>RowNumber</th>\n      <td>10000.0</td>\n      <td>5.000500e+03</td>\n      <td>2886.895680</td>\n      <td>1.00</td>\n      <td>5.000500e+03</td>\n      <td>10000.00</td>\n    </tr>\n    <tr>\n      <th>CustomerId</th>\n      <td>10000.0</td>\n      <td>1.569094e+07</td>\n      <td>71936.186123</td>\n      <td>15565701.00</td>\n      <td>1.569074e+07</td>\n      <td>15815690.00</td>\n    </tr>\n    <tr>\n      <th>CreditScore</th>\n      <td>10000.0</td>\n      <td>6.505288e+02</td>\n      <td>96.653299</td>\n      <td>350.00</td>\n      <td>6.520000e+02</td>\n      <td>850.00</td>\n    </tr>\n    <tr>\n      <th>Age</th>\n      <td>10000.0</td>\n      <td>3.892180e+01</td>\n      <td>10.487806</td>\n      <td>18.00</td>\n      <td>3.700000e+01</td>\n      <td>92.00</td>\n    </tr>\n    <tr>\n      <th>Tenure</th>\n      <td>9091.0</td>\n      <td>4.997690e+00</td>\n      <td>2.894723</td>\n      <td>0.00</td>\n      <td>5.000000e+00</td>\n      <td>10.00</td>\n    </tr>\n    <tr>\n      <th>Balance</th>\n      <td>10000.0</td>\n      <td>7.648589e+04</td>\n      <td>62397.405202</td>\n      <td>0.00</td>\n      <td>9.719854e+04</td>\n      <td>250898.09</td>\n    </tr>\n    <tr>\n      <th>NumOfProducts</th>\n      <td>10000.0</td>\n      <td>1.530200e+00</td>\n      <td>0.581654</td>\n      <td>1.00</td>\n      <td>1.000000e+00</td>\n      <td>4.00</td>\n    </tr>\n    <tr>\n      <th>HasCrCard</th>\n      <td>10000.0</td>\n      <td>7.055000e-01</td>\n      <td>0.455840</td>\n      <td>0.00</td>\n      <td>1.000000e+00</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>IsActiveMember</th>\n      <td>10000.0</td>\n      <td>5.151000e-01</td>\n      <td>0.499797</td>\n      <td>0.00</td>\n      <td>1.000000e+00</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>EstimatedSalary</th>\n      <td>10000.0</td>\n      <td>1.000902e+05</td>\n      <td>57510.492818</td>\n      <td>11.58</td>\n      <td>1.001939e+05</td>\n      <td>199992.48</td>\n    </tr>\n    <tr>\n      <th>Exited</th>\n      <td>10000.0</td>\n      <td>2.037000e-01</td>\n      <td>0.402769</td>\n      <td>0.00</td>\n      <td>0.000000e+00</td>\n      <td>1.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество дублированных строк: 0\n"
     ]
    }
   ],
   "source": [
    "describe_dataframe(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Признаки</b>\n",
    "RowNumber — индекс строки в данных\n",
    "CustomerId — уникальный идентификатор клиента\n",
    "Surname — фамилия\n",
    "CreditScore — кредитный рейтинг\n",
    "Geography — страна проживания\n",
    "Gender — пол\n",
    "Age — возраст\n",
    "Tenure — сколько лет человек является клиентом банка\n",
    "Balance — баланс на счёте\n",
    "NumOfProducts — количество продуктов банка, используемых клиентом\n",
    "HasCrCard — наличие кредитной карты\n",
    "IsActiveMember — активность клиента\n",
    "EstimatedSalary — предполагаемая зарплата\n",
    "\n",
    "<b>Целевой признак</b>\n",
    "Exited — факт ухода клиента"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод из исследования данных: в столбце Tenure недостает 9% значений - заменим их нулями. Также имеется 3 столбца с категориальными значениями, которые следует привести к числовому виду. Для этого подойдет метод One-Hot Encoding. Она подходит для решения нашей задачи: работает со всеми моделями. Это критично, так как проверять будем большое количество моделей с разными параметрами."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заполним нулями недостающие данные в колонке \"Tenure\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Tenure\"].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поделим датасет на признаки и искомое значение."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "target = df['Exited']\n",
    "features = df.drop(['Exited'], axis=1)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Разделим данные на обучающую, валидационную и тестовую выборки (с учетом дисбаланса)."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "features_train, features_valid, target_train, target_valid = train_test_split(\n",
    "    features, target, test_size=.2, random_state=12345, stratify=target)\n",
    "features_train, features_test, target_train, target_test = train_test_split(\n",
    "    features_train, target_train, test_size=.25, random_state=12345, stratify=target_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "categorical_columns = list(filter(lambda column: is_string_dtype(features_train[column]), features.columns))\n",
    "numeric_columns = list(filter(lambda column: not is_string_dtype(features_train[column]), features.columns))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Далее кодируем значения категориальных переменных."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist')",
      "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;infrequent_if_exist&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;infrequent_if_exist&#x27;)</pre></div></div></div></div></div>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe_encoder = OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist')\n",
    "ohe_encoder.fit(features_train[categorical_columns])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6000 entries, 5536 to 8193\n",
      "Columns: 2251 entries, RowNumber to Gender_Male\n",
      "dtypes: float64(2244), int64(7)\n",
      "memory usage: 103.1 MB\n"
     ]
    }
   ],
   "source": [
    "features_train_encoded = pd.DataFrame(\n",
    "    ohe_encoder.transform(features_train[categorical_columns]).toarray(),\n",
    "    columns=ohe_encoder.get_feature_names_out(categorical_columns),\n",
    "    index=features_train.index\n",
    ")\n",
    "features_train = pd.concat((features_train[numeric_columns], features_train_encoded), axis=1)\n",
    "features_train.info()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\APMuravev\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:202: UserWarning: Found unknown categories in columns [0] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2000 entries, 9375 to 1674\n",
      "Columns: 2251 entries, RowNumber to Gender_Male\n",
      "dtypes: float64(2244), int64(7)\n",
      "memory usage: 34.4 MB\n"
     ]
    }
   ],
   "source": [
    "features_test_encoded = pd.DataFrame(\n",
    "    ohe_encoder.transform(features_test[categorical_columns]).toarray(),\n",
    "    columns=ohe_encoder.get_feature_names_out(categorical_columns),\n",
    "    index=features_test.index\n",
    ")\n",
    "features_test = pd.concat(\n",
    "    (features_test[numeric_columns], features_test_encoded),\n",
    "    axis=1\n",
    ")\n",
    "features_test.info()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2000 entries, 7837 to 1729\n",
      "Columns: 2251 entries, RowNumber to Gender_Male\n",
      "dtypes: float64(2244), int64(7)\n",
      "memory usage: 34.4 MB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\APMuravev\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:202: UserWarning: Found unknown categories in columns [0] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "features_valid_encoded = pd.DataFrame(\n",
    "    ohe_encoder.transform(features_valid[categorical_columns]).toarray(),\n",
    "    columns=ohe_encoder.get_feature_names_out(categorical_columns),\n",
    "    index=features_valid.index\n",
    ")\n",
    "features_valid = pd.concat(\n",
    "    (features_valid[numeric_columns], features_valid_encoded),\n",
    "    axis=1\n",
    ")\n",
    "features_valid.info()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "target_test.index = features_test['RowNumber']\n",
    "features_test.index = features_test['RowNumber']\n",
    "features_test.drop(['RowNumber'], axis=1, inplace=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "target_train.index = features_train['RowNumber']\n",
    "features_train.index = features_train['RowNumber']\n",
    "features_train.drop(['RowNumber'], axis=1, inplace=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "target_valid.index = features_valid['RowNumber']\n",
    "features_valid.index = features_valid['RowNumber']\n",
    "features_valid.drop(['RowNumber'], axis=1, inplace=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные готовы к исследованию."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Исследование задачи"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим таргет на дисбаланс. Посчитаем отношение нулей и единиц."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.7965"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(target_test, pd.Series(0, index=target_test.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_test.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Нулей 80% от общего количества значений в таргете, наблюдается явный дисбаланс классов. В других выборках дисбаланс аналогичен."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для того чтобы у всех признаков был единый масштаб, стандартизируем их с помощью отдельной структуры для стандартизации данных sklearn.preprocessing.StandardScaler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "           CustomerId  CreditScore       Age    Tenure   Balance  \\\nRowNumber                                                          \n5537         1.454988    -0.143332  0.577533 -1.456283 -1.220573   \n8531         0.166309     1.632702 -0.564119 -0.813711  0.435807   \n1763         1.730513     1.116413 -0.468981 -1.134997  1.245822   \n9091         1.706152     1.643028  0.006707  0.150148 -1.220573   \n8778         0.440831    -0.484083 -1.420358 -1.134997  1.421989   \n6145        -1.031978     0.445237  0.672671 -0.492424  1.206938   \n5498         0.264738     1.281625 -0.278706  1.114006  0.653989   \n8657         1.391810     0.104487 -0.754394 -1.134997  0.132478   \n8834         0.967436    -0.577015  1.053222  0.792720  0.080479   \n6385         1.680553    -1.248190 -0.754394  0.792720 -1.220573   \n\n           NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\nRowNumber                                                              \n5537            0.797767   0.652576        0.967527         1.029613   \n8531           -0.916018   0.652576       -1.033563         0.237986   \n1763           -0.916018   0.652576        0.967527        -0.686104   \n9091           -0.916018   0.652576       -1.033563        -0.391097   \n8778            0.797767   0.652576       -1.033563        -1.361559   \n6145           -0.916018   0.652576       -1.033563         1.399264   \n5498           -0.916018  -1.532388       -1.033563         0.028082   \n8657           -0.916018   0.652576        0.967527         0.652887   \n8834           -0.916018   0.652576        0.967527         1.453685   \n6385            0.797767   0.652576        0.967527         1.311712   \n\n           Surname_Abbott  ...  Surname_Zotov  Surname_Zox  Surname_Zubarev  \\\nRowNumber                  ...                                                \n5537            -0.022366  ...      -0.012911    -0.012911        -0.022366   \n8531            -0.022366  ...      -0.012911    -0.012911        -0.022366   \n1763            -0.022366  ...      -0.012911    -0.012911        -0.022366   \n9091            -0.022366  ...      -0.012911    -0.012911        -0.022366   \n8778            -0.022366  ...      -0.012911    -0.012911        -0.022366   \n6145            -0.022366  ...      -0.012911    -0.012911        -0.022366   \n5498            -0.022366  ...      -0.012911    -0.012911        -0.022366   \n8657            -0.022366  ...      -0.012911    -0.012911        -0.022366   \n8834            -0.022366  ...      -0.012911    -0.012911        -0.022366   \n6385            -0.022366  ...      -0.012911    -0.012911        -0.022366   \n\n           Surname_Zubareva  Surname_Zuev  Surname_Zuyev  Surname_Zuyeva  \\\nRowNumber                                                                  \n5537              -0.012911     -0.012911       -0.01826       -0.012911   \n8531              -0.012911     -0.012911       -0.01826       -0.012911   \n1763              -0.012911     -0.012911       -0.01826       -0.012911   \n9091              -0.012911     -0.012911       -0.01826       -0.012911   \n8778              -0.012911     -0.012911       -0.01826       -0.012911   \n6145              -0.012911     -0.012911       -0.01826       -0.012911   \n5498              -0.012911     -0.012911       -0.01826       -0.012911   \n8657              -0.012911     -0.012911       -0.01826       -0.012911   \n8834              -0.012911     -0.012911       -0.01826       -0.012911   \n6385              -0.012911     -0.012911       -0.01826       -0.012911   \n\n           Geography_Germany  Geography_Spain  Gender_Male  \nRowNumber                                                   \n5537               -0.572988         1.734363     0.915552  \n8531               -0.572988        -0.576580    -1.092237  \n1763               -0.572988        -0.576580    -1.092237  \n9091               -0.572988        -0.576580    -1.092237  \n8778               -0.572988         1.734363     0.915552  \n6145               -0.572988         1.734363    -1.092237  \n5498               -0.572988         1.734363    -1.092237  \n8657               -0.572988         1.734363     0.915552  \n8834               -0.572988        -0.576580     0.915552  \n6385               -0.572988        -0.576580    -1.092237  \n\n[10 rows x 2250 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CustomerId</th>\n      <th>CreditScore</th>\n      <th>Age</th>\n      <th>Tenure</th>\n      <th>Balance</th>\n      <th>NumOfProducts</th>\n      <th>HasCrCard</th>\n      <th>IsActiveMember</th>\n      <th>EstimatedSalary</th>\n      <th>Surname_Abbott</th>\n      <th>...</th>\n      <th>Surname_Zotov</th>\n      <th>Surname_Zox</th>\n      <th>Surname_Zubarev</th>\n      <th>Surname_Zubareva</th>\n      <th>Surname_Zuev</th>\n      <th>Surname_Zuyev</th>\n      <th>Surname_Zuyeva</th>\n      <th>Geography_Germany</th>\n      <th>Geography_Spain</th>\n      <th>Gender_Male</th>\n    </tr>\n    <tr>\n      <th>RowNumber</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5537</th>\n      <td>1.454988</td>\n      <td>-0.143332</td>\n      <td>0.577533</td>\n      <td>-1.456283</td>\n      <td>-1.220573</td>\n      <td>0.797767</td>\n      <td>0.652576</td>\n      <td>0.967527</td>\n      <td>1.029613</td>\n      <td>-0.022366</td>\n      <td>...</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.022366</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.01826</td>\n      <td>-0.012911</td>\n      <td>-0.572988</td>\n      <td>1.734363</td>\n      <td>0.915552</td>\n    </tr>\n    <tr>\n      <th>8531</th>\n      <td>0.166309</td>\n      <td>1.632702</td>\n      <td>-0.564119</td>\n      <td>-0.813711</td>\n      <td>0.435807</td>\n      <td>-0.916018</td>\n      <td>0.652576</td>\n      <td>-1.033563</td>\n      <td>0.237986</td>\n      <td>-0.022366</td>\n      <td>...</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.022366</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.01826</td>\n      <td>-0.012911</td>\n      <td>-0.572988</td>\n      <td>-0.576580</td>\n      <td>-1.092237</td>\n    </tr>\n    <tr>\n      <th>1763</th>\n      <td>1.730513</td>\n      <td>1.116413</td>\n      <td>-0.468981</td>\n      <td>-1.134997</td>\n      <td>1.245822</td>\n      <td>-0.916018</td>\n      <td>0.652576</td>\n      <td>0.967527</td>\n      <td>-0.686104</td>\n      <td>-0.022366</td>\n      <td>...</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.022366</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.01826</td>\n      <td>-0.012911</td>\n      <td>-0.572988</td>\n      <td>-0.576580</td>\n      <td>-1.092237</td>\n    </tr>\n    <tr>\n      <th>9091</th>\n      <td>1.706152</td>\n      <td>1.643028</td>\n      <td>0.006707</td>\n      <td>0.150148</td>\n      <td>-1.220573</td>\n      <td>-0.916018</td>\n      <td>0.652576</td>\n      <td>-1.033563</td>\n      <td>-0.391097</td>\n      <td>-0.022366</td>\n      <td>...</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.022366</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.01826</td>\n      <td>-0.012911</td>\n      <td>-0.572988</td>\n      <td>-0.576580</td>\n      <td>-1.092237</td>\n    </tr>\n    <tr>\n      <th>8778</th>\n      <td>0.440831</td>\n      <td>-0.484083</td>\n      <td>-1.420358</td>\n      <td>-1.134997</td>\n      <td>1.421989</td>\n      <td>0.797767</td>\n      <td>0.652576</td>\n      <td>-1.033563</td>\n      <td>-1.361559</td>\n      <td>-0.022366</td>\n      <td>...</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.022366</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.01826</td>\n      <td>-0.012911</td>\n      <td>-0.572988</td>\n      <td>1.734363</td>\n      <td>0.915552</td>\n    </tr>\n    <tr>\n      <th>6145</th>\n      <td>-1.031978</td>\n      <td>0.445237</td>\n      <td>0.672671</td>\n      <td>-0.492424</td>\n      <td>1.206938</td>\n      <td>-0.916018</td>\n      <td>0.652576</td>\n      <td>-1.033563</td>\n      <td>1.399264</td>\n      <td>-0.022366</td>\n      <td>...</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.022366</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.01826</td>\n      <td>-0.012911</td>\n      <td>-0.572988</td>\n      <td>1.734363</td>\n      <td>-1.092237</td>\n    </tr>\n    <tr>\n      <th>5498</th>\n      <td>0.264738</td>\n      <td>1.281625</td>\n      <td>-0.278706</td>\n      <td>1.114006</td>\n      <td>0.653989</td>\n      <td>-0.916018</td>\n      <td>-1.532388</td>\n      <td>-1.033563</td>\n      <td>0.028082</td>\n      <td>-0.022366</td>\n      <td>...</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.022366</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.01826</td>\n      <td>-0.012911</td>\n      <td>-0.572988</td>\n      <td>1.734363</td>\n      <td>-1.092237</td>\n    </tr>\n    <tr>\n      <th>8657</th>\n      <td>1.391810</td>\n      <td>0.104487</td>\n      <td>-0.754394</td>\n      <td>-1.134997</td>\n      <td>0.132478</td>\n      <td>-0.916018</td>\n      <td>0.652576</td>\n      <td>0.967527</td>\n      <td>0.652887</td>\n      <td>-0.022366</td>\n      <td>...</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.022366</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.01826</td>\n      <td>-0.012911</td>\n      <td>-0.572988</td>\n      <td>1.734363</td>\n      <td>0.915552</td>\n    </tr>\n    <tr>\n      <th>8834</th>\n      <td>0.967436</td>\n      <td>-0.577015</td>\n      <td>1.053222</td>\n      <td>0.792720</td>\n      <td>0.080479</td>\n      <td>-0.916018</td>\n      <td>0.652576</td>\n      <td>0.967527</td>\n      <td>1.453685</td>\n      <td>-0.022366</td>\n      <td>...</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.022366</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.01826</td>\n      <td>-0.012911</td>\n      <td>-0.572988</td>\n      <td>-0.576580</td>\n      <td>0.915552</td>\n    </tr>\n    <tr>\n      <th>6385</th>\n      <td>1.680553</td>\n      <td>-1.248190</td>\n      <td>-0.754394</td>\n      <td>0.792720</td>\n      <td>-1.220573</td>\n      <td>0.797767</td>\n      <td>0.652576</td>\n      <td>0.967527</td>\n      <td>1.311712</td>\n      <td>-0.022366</td>\n      <td>...</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.022366</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>-0.01826</td>\n      <td>-0.012911</td>\n      <td>-0.572988</td>\n      <td>-0.576580</td>\n      <td>-1.092237</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 2250 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6000 entries, 5537 to 8194\n",
      "Columns: 2250 entries, CustomerId to Gender_Male\n",
      "dtypes: float64(2250)\n",
      "memory usage: 103.0 MB\n"
     ]
    },
    {
     "data": {
      "text/plain": "None"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "                    count          mean       std       min       50%  \\\nCustomerId         6000.0 -9.061788e-15  1.000083 -1.750860 -0.002715   \nCreditScore        6000.0  2.925068e-16  1.000083 -3.096505  0.011555   \nAge                6000.0  2.581639e-16  1.000083 -1.991184 -0.183568   \nTenure             6000.0  2.723747e-17  1.000083 -1.456283 -0.171138   \nBalance            6000.0  1.207923e-16  1.000083 -1.220573  0.334671   \n...                   ...           ...       ...       ...       ...   \nSurname_Zuyev      6000.0 -1.184238e-18  1.000083 -0.018260 -0.018260   \nSurname_Zuyeva     6000.0  3.552714e-18  1.000083 -0.012911 -0.012911   \nGeography_Germany  6000.0  5.684342e-17  1.000083 -0.572988 -0.572988   \nGeography_Spain    6000.0 -3.907985e-17  1.000083 -0.576580 -0.576580   \nGender_Male        6000.0  1.125026e-17  1.000083 -1.092237  0.915552   \n\n                         max  \nCustomerId          1.730513  \nCreditScore         2.066384  \nAge                 5.049004  \nTenure              1.756578  \nBalance             2.794064  \n...                      ...  \nSurname_Zuyev      54.763126  \nSurname_Zuyeva     77.453212  \nGeography_Germany   1.745237  \nGeography_Spain     1.734363  \nGender_Male         0.915552  \n\n[2250 rows x 6 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>50%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>CustomerId</th>\n      <td>6000.0</td>\n      <td>-9.061788e-15</td>\n      <td>1.000083</td>\n      <td>-1.750860</td>\n      <td>-0.002715</td>\n      <td>1.730513</td>\n    </tr>\n    <tr>\n      <th>CreditScore</th>\n      <td>6000.0</td>\n      <td>2.925068e-16</td>\n      <td>1.000083</td>\n      <td>-3.096505</td>\n      <td>0.011555</td>\n      <td>2.066384</td>\n    </tr>\n    <tr>\n      <th>Age</th>\n      <td>6000.0</td>\n      <td>2.581639e-16</td>\n      <td>1.000083</td>\n      <td>-1.991184</td>\n      <td>-0.183568</td>\n      <td>5.049004</td>\n    </tr>\n    <tr>\n      <th>Tenure</th>\n      <td>6000.0</td>\n      <td>2.723747e-17</td>\n      <td>1.000083</td>\n      <td>-1.456283</td>\n      <td>-0.171138</td>\n      <td>1.756578</td>\n    </tr>\n    <tr>\n      <th>Balance</th>\n      <td>6000.0</td>\n      <td>1.207923e-16</td>\n      <td>1.000083</td>\n      <td>-1.220573</td>\n      <td>0.334671</td>\n      <td>2.794064</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>Surname_Zuyev</th>\n      <td>6000.0</td>\n      <td>-1.184238e-18</td>\n      <td>1.000083</td>\n      <td>-0.018260</td>\n      <td>-0.018260</td>\n      <td>54.763126</td>\n    </tr>\n    <tr>\n      <th>Surname_Zuyeva</th>\n      <td>6000.0</td>\n      <td>3.552714e-18</td>\n      <td>1.000083</td>\n      <td>-0.012911</td>\n      <td>-0.012911</td>\n      <td>77.453212</td>\n    </tr>\n    <tr>\n      <th>Geography_Germany</th>\n      <td>6000.0</td>\n      <td>5.684342e-17</td>\n      <td>1.000083</td>\n      <td>-0.572988</td>\n      <td>-0.572988</td>\n      <td>1.745237</td>\n    </tr>\n    <tr>\n      <th>Geography_Spain</th>\n      <td>6000.0</td>\n      <td>-3.907985e-17</td>\n      <td>1.000083</td>\n      <td>-0.576580</td>\n      <td>-0.576580</td>\n      <td>1.734363</td>\n    </tr>\n    <tr>\n      <th>Gender_Male</th>\n      <td>6000.0</td>\n      <td>1.125026e-17</td>\n      <td>1.000083</td>\n      <td>-1.092237</td>\n      <td>0.915552</td>\n      <td>0.915552</td>\n    </tr>\n  </tbody>\n</table>\n<p>2250 rows × 6 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество дублированных строк: 0\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(features_train)\n",
    "\n",
    "features_train = pd.DataFrame(\n",
    "    scaler.transform(features_train),\n",
    "    columns=features_train.columns,\n",
    "    index=features_train.index)\n",
    "\n",
    "features_test = pd.DataFrame(\n",
    "    scaler.transform(features_test),\n",
    "    columns=features_test.columns,\n",
    "    index=features_test.index)\n",
    "\n",
    "features_valid = pd.DataFrame(\n",
    "    scaler.transform(features_valid),\n",
    "    columns=features_valid.columns,\n",
    "    index=features_valid.index)\n",
    "\n",
    "describe_dataframe(features_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Попробуем обучить модели без учета дисбаланса и просмотрим чему будет равна метрика F1. Так как перед нами стоит задача классификации, то будем использовать 3 модели: Логистическую регрессию, Дерево решений и Случайный лес."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Для построения roc кривой пропишем специальную функцию."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def roc_graph(tv, pv):\n",
    "    fpr, tpr, thresholds = roc_curve(tv, pv)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "    plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0, 1])\n",
    "    plt.ylim([0, 1])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "kf = KFold(n_splits=5, random_state=32123, shuffle=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "params = {'solver': ['liblinear'],\n",
    "          'random_state': [12345]}"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "lr_pipeline = make_pipeline(LogisticRegression())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [],
   "source": [
    "lr_params = {'logisticregression__' + key: params[key] for key in params}\n",
    "grid_lr = GridSearchCV(lr_pipeline, param_grid=lr_params, cv=kf, scoring='f1',\n",
    "                       return_train_score=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "{'logisticregression__random_state': 12345,\n 'logisticregression__solver': 'liblinear'}"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_lr.fit(features_train, target_train)\n",
    "grid_lr.best_params_"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "'Средняя оценка лучшей lr модели: 0.355410941211441'"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"Средняя оценка лучшей lr модели: {grid_lr.best_score_}\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обучим модель логистической регрессии. (LR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У модели мала метрика F1, такая модель не соответствует требуемой."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обучим модель дерева решений. (DT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "params = {\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'max_depth': range(2, 18, 2),\n",
    "    'random_state': [32123]\n",
    "}"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [],
   "source": [
    "dt_pipeline = make_pipeline(DecisionTreeClassifier())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "new_params = {'decisiontreeclassifier__' + key: params[key] for key in params}\n",
    "grid_dt = GridSearchCV(dt_pipeline, param_grid=new_params, cv=kf, scoring='f1',\n",
    "                       return_train_score=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "{'decisiontreeclassifier__criterion': 'entropy',\n 'decisiontreeclassifier__max_depth': 8,\n 'decisiontreeclassifier__random_state': 32123}"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_dt.fit(features_train, target_train)\n",
    "grid_dt.best_params_"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "'Средняя оценка лучшей dt модели: 0.5517254672121622, Глубина дерева: 8, Лучший критерий: entropy'"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"Средняя оценка лучшей dt модели: {grid_dt.best_score_}, Глубина дерева: {grid_dt.best_params_['decisiontreeclassifier__max_depth']}, Лучший критерий: {grid_dt.best_params_['decisiontreeclassifier__criterion']}\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сразу же получаем результат, близкий к требуемому. Неплохо, но все равно мало."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обучим модель случайного леса. (RF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [],
   "source": [
    "params = {'n_estimators': range(50, 250, 50),\n",
    "          'max_depth': range(4, 14, 2),\n",
    "          'random_state': [32123]}"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [],
   "source": [
    "rf_pipeline = make_pipeline(RandomForestClassifier())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [],
   "source": [
    "rf_params = {'randomforestclassifier__' + key: params[key] for key in params}\n",
    "grid_rf = GridSearchCV(rf_pipeline, param_grid=rf_params, cv=kf, scoring='f1',\n",
    "                       return_train_score=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "data": {
      "text/plain": "{'randomforestclassifier__max_depth': 12,\n 'randomforestclassifier__n_estimators': 200,\n 'randomforestclassifier__random_state': 32123}"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_rf.fit(features_train, target_train)\n",
    "grid_rf.best_params_"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "'Средняя оценка лучшей rf модели: 0.8038371742252739, Глубина дерева: 12, Измерители: 200'"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"Средняя оценка лучшей rf модели: {grid_rf.best_score_}, Глубина дерева: {grid_rf.best_params_['randomforestclassifier__max_depth']}, Измерители: {grid_rf.best_params_['randomforestclassifier__n_estimators']}\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь рассмотрим модели, сбалансировав классы."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводы: Дисбаланс и мультиколлинеарность - это проблема для линейных моделей, по типу логистической (LR) и линейной регрессий. Скорее всего в данном датасете более сложные, нелинейные связи между признаками и таргетом. \"Деревянные\" модели (RF, DT) устойчивы к мультиколлинеарности, данные модели гораздо лучше показывают себя при существующем дисбалансе классов. На других данных LR могла оказаться лучше, например потому что RF не может экстраполировать."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Борьба с дисбалансом"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для борьбы с дисбалансом есть несколько способов: взвешивание класса, увеличение выборки, уменьшение выборки и изменение порога принятия решения.\n",
    "В данном конкретном случае воспользуемся готовым классом SMOTE, который автоматически устранит дисбаланс данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE(random_state=12345)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Изучение моделей логистической регрессии при учёте дисбаланса"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Протестируем модель логистической регрессии на сбалансированных данных:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [],
   "source": [
    "params = {'solver': ['liblinear'],\n",
    "          'random_state': [12345]}"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_pipeline = make_pipeline(smote, LogisticRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [],
   "source": [
    "lr_params = {'logisticregression__' + key: params[key] for key in params}\n",
    "grid_lr = GridSearchCV(lr_pipeline, param_grid=lr_params, cv=kf, scoring='f1',\n",
    "                       return_train_score=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [
    {
     "data": {
      "text/plain": "{'logisticregression__random_state': 12345,\n 'logisticregression__solver': 'liblinear'}"
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_lr.fit(features_train, target_train)\n",
    "grid_lr.best_params_"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "data": {
      "text/plain": "'Средняя оценка лучшей lr модели: 0.3555349384214359'"
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"Средняя оценка лучшей lr модели: {grid_lr.best_score_}\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "0.32808398950131235"
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_predicted_valid = grid_lr.predict(features_valid)\n",
    "roc_graph(target_valid, best_predicted_valid)\n",
    "f1_score(target_valid, best_predicted_valid)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Модель показывает крайне низкие результаты."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применим встроенное взвешивание классов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "'F1: 0.3767491926803014'"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "array([[1246,  347],\n       [ 232,  175]], dtype=int64)"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = LogisticRegression(random_state=12345, solver='liblinear', class_weight='balanced')\n",
    "model.fit(features_train, target_train)\n",
    "predicted_valid = model.predict(features_valid)\n",
    "display(f\"F1: {f1_score(target_valid, predicted_valid)}\")\n",
    "display(confusion_matrix(target_valid, predicted_valid))\n",
    "\n",
    "probabilities_test = model.predict_proba(features_valid)\n",
    "probabilities_one_test = probabilities_test[:, 1]\n",
    "roc_graph(target_valid, predicted_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Качество модели немного повысилось, но оно не дотягивает до требуемого.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Протестируем изменения порога принятия решения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = LogisticRegression(random_state=12345, solver='liblinear')\n",
    "model.fit(features_train, target_train)\n",
    "probabilities_valid = model.predict_proba(features_valid)\n",
    "probabilities_one_valid = probabilities_valid[:, 1]\n",
    "threshold_target_results = []\n",
    "for threshold in np.arange(0, 0.4, 0.04):\n",
    "    predicted_valid = probabilities_one_valid > threshold\n",
    "    score = f1_score(target_valid, predicted_valid)\n",
    "    threshold_target_results.append(score)\n",
    "plt.plot(threshold_target_results)\n",
    "plt.ylabel('F1 score')\n",
    "plt.xlabel('steps')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Изменение порога также не даёт видимого эффекта. В лучшем случае качество модели сопоставимо с моделью, обученной на уменьшенном/увеличенном датасете."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Изучение моделей случайного леса при учёте дисбаланса"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'n_estimators': range(50, 250, 50),\n",
    "          'max_depth': range(4, 14, 2),\n",
    "          'random_state': [12345]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [],
   "source": [
    "rf_pipeline = make_pipeline(SMOTE(random_state=12345),\n",
    "                            RandomForestClassifier())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "outputs": [],
   "source": [
    "rf_params = {'randomforestclassifier__' + key: params[key] for key in params}\n",
    "grid_rf = GridSearchCV(rf_pipeline, param_grid=rf_params, cv=kf, scoring='f1',\n",
    "                       return_train_score=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "data": {
      "text/plain": "{'randomforestclassifier__max_depth': 6,\n 'randomforestclassifier__n_estimators': 50,\n 'randomforestclassifier__random_state': 12345}"
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_rf.fit(features_train, target_train)\n",
    "grid_rf.best_params_"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "data": {
      "text/plain": "'Средняя оценка лучшей rf модели: 0.39904721039796687, Глубина дерева: 6, Измерители: 50'"
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"Средняя оценка лучшей rf модели: {grid_rf.best_score_}, Глубина дерева: {grid_rf.best_params_['randomforestclassifier__max_depth']}, Измерители: {grid_rf.best_params_['randomforestclassifier__n_estimators']}\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "0.41739130434782606"
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_predicted_valid = grid_rf.predict(features_valid)\n",
    "roc_graph(target_valid, best_predicted_valid)\n",
    "f1_score(target_valid, best_predicted_valid)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводы: Наилучшим образом себя показали модели случайного леса, все три получившиеся метрики F1 близки к требуемым. Выводы исследования подтвердились, встроенная функция взвешивания классов позволяет получить наилучшую модель, точность метрики F1 которой соответствует требованиям."
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Изучение модели дерева решений при учёте дисбаланса"
   ],
   "metadata": {
    "collapsed": false
   },
   "execution_count": 75
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [],
   "source": [
    "params = {\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'max_depth': range(2, 18, 2),\n",
    "    'random_state': [12345]\n",
    "}"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [],
   "source": [
    "dt_pipeline = make_pipeline(SMOTE(random_state=12345),\n",
    "                            DecisionTreeClassifier())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [],
   "source": [
    "new_params = {'decisiontreeclassifier__' + key: params[key] for key in params}\n",
    "grid_dt = GridSearchCV(dt_pipeline, param_grid=new_params, cv=kf, scoring='f1',\n",
    "                       return_train_score=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [
    {
     "data": {
      "text/plain": "{'decisiontreeclassifier__criterion': 'entropy',\n 'decisiontreeclassifier__max_depth': 12,\n 'decisiontreeclassifier__random_state': 12345}"
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_dt.fit(features_train, target_train)\n",
    "grid_dt.best_params_"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [
    {
     "data": {
      "text/plain": "'Средняя оценка лучшей dt модели: 0.566118475671362, Глубина дерева: 12, Лучший критерий: entropy'"
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"Средняя оценка лучшей dt модели: {grid_dt.best_score_}, Глубина дерева: {grid_dt.best_params_['decisiontreeclassifier__max_depth']}, Лучший критерий: {grid_dt.best_params_['decisiontreeclassifier__criterion']}\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "0.5501432664756447"
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_predicted_valid = grid_dt.predict(features_valid)\n",
    "roc_graph(target_valid, best_predicted_valid)\n",
    "f1_score(target_valid, best_predicted_valid)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Тестирование модели"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Протестируем модель на тестовой выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_dt_model = DecisionTreeClassifier(\n",
    "    random_state=12345,\n",
    "    max_depth=grid_dt.best_params_['decisiontreeclassifier__max_depth'],\n",
    "    criterion=grid_dt.best_params_['decisiontreeclassifier__criterion'],\n",
    "    splitter='best')\n",
    "best_dt_model.fit(features_train, target_train)\n",
    "best_predicted_valid = best_dt_model.predict(features_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [
    {
     "data": {
      "text/plain": "'f1 score наилучшей модели на тестовой выборке: 0.5185185185185185'"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_dt_model_f1 = f1_score(target_test, best_predicted_valid)\n",
    "display(f\"f1 score наилучшей модели на тестовой выборке: {best_dt_model_f1}\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_graph(target_test, best_predicted_valid)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Задача провалена."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь попробуем дообучить её с использованием валидационных данных (они нам больше не понадобятся). После чего заново определим наилучшие гиперпараметры модели, это может повысить значение метрики F1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "outputs": [
    {
     "data": {
      "text/plain": "{'decisiontreeclassifier__criterion': 'gini',\n 'decisiontreeclassifier__max_depth': 6,\n 'decisiontreeclassifier__random_state': 12345}"
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_params = {'decisiontreeclassifier__' + key: params[key] for key in params}\n",
    "grid_dt = GridSearchCV(dt_pipeline, param_grid=new_params, cv=kf, scoring='f1',\n",
    "                       return_train_score=True)\n",
    "grid_dt.fit(pd.concat([features_train, features_valid]), pd.concat([target_train, target_valid]))\n",
    "grid_dt.best_params_"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "outputs": [],
   "source": [
    "best_dt_model = DecisionTreeClassifier(\n",
    "    random_state=12345,\n",
    "    max_depth=grid_dt.best_params_['decisiontreeclassifier__max_depth'],\n",
    "    criterion=grid_dt.best_params_['decisiontreeclassifier__criterion'],\n",
    "    splitter='best')\n",
    "best_dt_model.fit(pd.concat([features_train, features_valid]), pd.concat([target_train, target_valid]))\n",
    "best_predicted_valid = best_dt_model.predict(features_test)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "outputs": [
    {
     "data": {
      "text/plain": "'f1 score наилучшей модели на тестовой выборке: 0.559375'"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_dt_model_f1 = f1_score(target_test, best_predicted_valid)\n",
    "display(f\"f1 score наилучшей модели на тестовой выборке: {best_dt_model_f1}\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_graph(target_test, best_predicted_valid)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Дообучив модель, была получена метрика f1 = 0.56."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводы: В данной работе необходимо было построить модель для задачи классификации, которая спрогнозирует, уйдёт клиент из банка в ближайшее время или нет. Для решения задачи необходимо построить модель с максимально большим значением метрики F1. Чтобы сдать проект успешно, нужно довести метрику до 0.59. Была проведена работа по изучению и разделению данных на выборки для обучения и тестирования моделей, перед этим было проведено кодирование категориальных значений, а также исключены пропуски в данных. Был исследован и устранен дисбаланс данных, далее для каждой модели был проведён подбор оптимальных гиперпараметров, после чего была выявлена оптимальная модель с наибольшим значением метрики F1 = 0.56, что ниже требуемой 0.59. Данная модель была проверена на тестовых данных, а также была оценена AUC-ROC метрика, она равна 0.70, что является хорошим показателем при метрике F1=0.56, из чего следует, что модель получилась точная. Две лучших набора гиперпараметров: 1) F1 = 0.04 Количество деревьев: 50 Максимальная глубина: 6 2) F1 = 0.4 3) F1 = 0.56 Критерий: 'gini' Максимальная глубина: 6 .Предпочтительнее выбрать модель дерева решений, в ней метрика F1 близка требуемой."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Чек-лист готовности проекта"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поставьте 'x' в выполненных пунктах. Далее нажмите Shift+Enter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x]  Jupyter Notebook открыт\n",
    "- [x]  Весь код выполняется без ошибок\n",
    "- [x]  Ячейки с кодом расположены в порядке исполнения\n",
    "- [x]  Выполнен шаг 1: данные подготовлены\n",
    "- [x]  Выполнен шаг 2: задача исследована\n",
    "    - [x]  Исследован баланс классов\n",
    "    - [x]  Изучены модели без учёта дисбаланса\n",
    "    - [x]  Написаны выводы по результатам исследования\n",
    "- [x]  Выполнен шаг 3: учтён дисбаланс\n",
    "    - [x]  Применено несколько способов борьбы с дисбалансом\n",
    "    - [x]  Написаны выводы по результатам исследования\n",
    "- [x]  Выполнен шаг 4: проведено тестирование\n",
    "- [x]  Удалось достичь *F1*-меры не менее 0.59\n",
    "- [x]  Исследована метрика *AUC-ROC*"
   ]
  }
 ],
 "metadata": {
  "ExecuteTimeLog": [
   {
    "duration": 1628,
    "start_time": "2023-03-22T17:29:53.158Z"
   },
   {
    "duration": 391,
    "start_time": "2023-03-22T17:29:54.788Z"
   },
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